EMG Signal Feature Extraction, Normalization and Classification for Pain and Normal Muscles Using Genetic Algorithm and Support Vector Machine
Bibliographic record
Abstract
Electromyography (EMG) is the process of measuring neuromuscular activities generated during the contraction and expansion period of muscles throughout the body. The potential is recorded by inserting needle or by placing electrodes on the surface of body. In this research, an automatic EMG signal classification system is developed using machine learning oriented Support Vector Machine (SVM). The collected data is selected using Genetic Algorithm (GA). The purpose of GA is to select those rows from the dataset, which contains potential or electrical activities recorded while the patient is in motion. Furthermore, the selected features are neutralized using critic method. To improve the row selection cosine similarity is being used to determine an average value hence also helps for data reduction. Based on the average similarity values, SVM is trained and used for classification during the testing phase. The experiment has been performed in MATLAB tool and the classification accuracy for normal and pain EMG signal of 91.3% and 92.4% respectively is achieved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".